HERMES: data placement and schema optimization for enterprise knowledge bases

Abstract

Enterprises create domain-specific knowledge bases (KBs) by curating and integrating their business data from multiple sources. To support a variety of query types over domain-specific KBs, we propose Hermes, an ontology-based system that allows storing KB data in multiple backends, and querying them with different query languages. In this paper, we address two important challenges in realizing such a system: data placement and schema optimization. First, we identify the best data store for any query type and determine the subset of the KB that needs to be stored in this data store, while minimizing data replication. Second, we optimize how we organize the data for best query performance. To choose the best data stores, we partition the data described by the domain ontology into multiple overlapping subsets based on the operations performed in a given query workload, and place these subsets in appropriate data stores according to their capabilities. Then, we optimize the schema on each data store to enable efficient querying. In particular, we focus on the property graph schema optimization, which has been largely ignored in the literature. We propose two algorithms to generate an optimized schema from the domain ontology. We demonstrate the effectiveness of our data placement and schema optimization algorithms with two real-world KBs from the medical and financial domains. The results show that the proposed data placement algorithm generates near-optimal data placement plans with minimal data replication overhead, and the schema optimization algorithms produce high-quality schemas, achieving up to two orders of magnitude speed-up compared to alternative schema designs.